Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations2215
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory502.0 KiB
Average record size in memory232.1 B

Variable types

Numeric15
Categorical13
DateTime1

Alerts

Z_CostContact has constant value "3"Constant
Z_Revenue has constant value "11"Constant
AcceptedCmp5 is highly overall correlated with Income and 1 other fieldsHigh correlation
Income is highly overall correlated with AcceptedCmp5 and 10 other fieldsHigh correlation
MntFishProducts is highly overall correlated with Income and 7 other fieldsHigh correlation
MntFruits is highly overall correlated with Income and 7 other fieldsHigh correlation
MntGoldProds is highly overall correlated with Income and 8 other fieldsHigh correlation
MntMeatProducts is highly overall correlated with Income and 8 other fieldsHigh correlation
MntSweetProducts is highly overall correlated with Income and 7 other fieldsHigh correlation
MntWines is highly overall correlated with AcceptedCmp5 and 9 other fieldsHigh correlation
NumCatalogPurchases is highly overall correlated with Income and 9 other fieldsHigh correlation
NumStorePurchases is highly overall correlated with Income and 8 other fieldsHigh correlation
NumWebPurchases is highly overall correlated with Income and 5 other fieldsHigh correlation
NumWebVisitsMonth is highly overall correlated with Income and 1 other fieldsHigh correlation
AcceptedCmp3 is highly imbalanced (62.1%)Imbalance
AcceptedCmp4 is highly imbalanced (61.9%)Imbalance
AcceptedCmp5 is highly imbalanced (62.2%)Imbalance
AcceptedCmp1 is highly imbalanced (65.6%)Imbalance
AcceptedCmp2 is highly imbalanced (89.7%)Imbalance
Complain is highly imbalanced (92.3%)Imbalance
ID has unique valuesUnique
Recency has 28 (1.3%) zerosZeros
MntFruits has 395 (17.8%) zerosZeros
MntFishProducts has 379 (17.1%) zerosZeros
MntSweetProducts has 413 (18.6%) zerosZeros
MntGoldProds has 61 (2.8%) zerosZeros
NumDealsPurchases has 44 (2.0%) zerosZeros
NumWebPurchases has 48 (2.2%) zerosZeros
NumCatalogPurchases has 576 (26.0%) zerosZeros

Reproduction

Analysis started2024-08-29 13:12:46.965144
Analysis finished2024-08-29 13:14:05.602368
Duration1 minute and 18.64 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIQUE 

Distinct2215
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5586.6181
Minimum0
Maximum11191
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:05.796896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile572.5
Q12814.5
median5455
Q38419
95-th percentile10676.6
Maximum11191
Range11191
Interquartile range (IQR)5604.5

Descriptive statistics

Standard deviation3249.0828
Coefficient of variation (CV)0.58158313
Kurtosis-1.1890012
Mean5586.6181
Median Absolute Deviation (MAD)2786
Skewness0.041343723
Sum12374359
Variance10556539
MonotonicityNot monotonic
2024-08-29T13:14:06.922609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1503 1
 
< 0.1%
9246 1
 
< 0.1%
2392 1
 
< 0.1%
1920 1
 
< 0.1%
5975 1
 
< 0.1%
5846 1
 
< 0.1%
2456 1
 
< 0.1%
938 1
 
< 0.1%
983 1
 
< 0.1%
9955 1
 
< 0.1%
Other values (2205) 2205
99.5%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
35 1
< 0.1%
ValueCountFrequency (%)
11191 1
< 0.1%
11188 1
< 0.1%
11187 1
< 0.1%
11181 1
< 0.1%
11178 1
< 0.1%
11176 1
< 0.1%
11171 1
< 0.1%
11166 1
< 0.1%
11148 1
< 0.1%
11133 1
< 0.1%

Year_Birth
Real number (ℝ)

Distinct59
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.8167
Minimum1893
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:07.276439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1893
5-th percentile1950
Q11959
median1970
Q31977
95-th percentile1988
Maximum1996
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.987
Coefficient of variation (CV)0.0060884284
Kurtosis0.7340236
Mean1968.8167
Median Absolute Deviation (MAD)9
Skewness-0.35291197
Sum4360929
Variance143.68816
MonotonicityNot monotonic
2024-08-29T13:14:07.777501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1976 89
 
4.0%
1971 86
 
3.9%
1975 83
 
3.7%
1972 78
 
3.5%
1978 76
 
3.4%
1970 75
 
3.4%
1965 74
 
3.3%
1973 72
 
3.3%
1969 70
 
3.2%
1974 69
 
3.1%
Other values (49) 1443
65.1%
ValueCountFrequency (%)
1893 1
 
< 0.1%
1899 1
 
< 0.1%
1900 1
 
< 0.1%
1940 1
 
< 0.1%
1941 1
 
< 0.1%
1943 6
 
0.3%
1944 7
0.3%
1945 8
0.4%
1946 16
0.7%
1947 16
0.7%
ValueCountFrequency (%)
1996 2
 
0.1%
1995 5
 
0.2%
1994 3
 
0.1%
1993 5
 
0.2%
1992 13
0.6%
1991 15
0.7%
1990 18
0.8%
1989 29
1.3%
1988 29
1.3%
1987 27
1.2%

Education
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Graduation
1115 
PhD
481 
Master
365 
2n Cycle
200 
Basic
 
54

Length

Max length10
Median length10
Mean length7.5182844
Min length3

Characters and Unicode

Total characters16653
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhD
2nd rowPhD
3rd rowMaster
4th rowPhD
5th rowGraduation

Common Values

ValueCountFrequency (%)
Graduation 1115
50.3%
PhD 481
21.7%
Master 365
 
16.5%
2n Cycle 200
 
9.0%
Basic 54
 
2.4%

Length

2024-08-29T13:14:08.274449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:08.828594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
graduation 1115
46.2%
phd 481
19.9%
master 365
 
15.1%
2n 200
 
8.3%
cycle 200
 
8.3%
basic 54
 
2.2%

Most occurring characters

ValueCountFrequency (%)
a 2649
15.9%
r 1480
8.9%
t 1480
8.9%
n 1315
 
7.9%
i 1169
 
7.0%
G 1115
 
6.7%
d 1115
 
6.7%
u 1115
 
6.7%
o 1115
 
6.7%
e 565
 
3.4%
Other values (12) 3535
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16653
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2649
15.9%
r 1480
8.9%
t 1480
8.9%
n 1315
 
7.9%
i 1169
 
7.0%
G 1115
 
6.7%
d 1115
 
6.7%
u 1115
 
6.7%
o 1115
 
6.7%
e 565
 
3.4%
Other values (12) 3535
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16653
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2649
15.9%
r 1480
8.9%
t 1480
8.9%
n 1315
 
7.9%
i 1169
 
7.0%
G 1115
 
6.7%
d 1115
 
6.7%
u 1115
 
6.7%
o 1115
 
6.7%
e 565
 
3.4%
Other values (12) 3535
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16653
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2649
15.9%
r 1480
8.9%
t 1480
8.9%
n 1315
 
7.9%
i 1169
 
7.0%
G 1115
 
6.7%
d 1115
 
6.7%
u 1115
 
6.7%
o 1115
 
6.7%
e 565
 
3.4%
Other values (12) 3535
21.2%

Marital_Status
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Married
857 
Together
572 
Single
471 
Divorced
232 
Widow
 
76
Other values (3)
 
7

Length

Max length8
Median length7
Mean length7.075395
Min length4

Characters and Unicode

Total characters15672
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTogether
2nd rowMarried
3rd rowTogether
4th rowMarried
5th rowTogether

Common Values

ValueCountFrequency (%)
Married 857
38.7%
Together 572
25.8%
Single 471
21.3%
Divorced 232
 
10.5%
Widow 76
 
3.4%
Alone 3
 
0.1%
Absurd 2
 
0.1%
YOLO 2
 
0.1%

Length

2024-08-29T13:14:09.352939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:09.949733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
married 857
38.7%
together 572
25.8%
single 471
21.3%
divorced 232
 
10.5%
widow 76
 
3.4%
alone 3
 
0.1%
absurd 2
 
0.1%
yolo 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 2707
17.3%
r 2520
16.1%
i 1636
10.4%
d 1167
7.4%
g 1043
 
6.7%
o 883
 
5.6%
M 857
 
5.5%
a 857
 
5.5%
T 572
 
3.6%
t 572
 
3.6%
Other values (16) 2858
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2707
17.3%
r 2520
16.1%
i 1636
10.4%
d 1167
7.4%
g 1043
 
6.7%
o 883
 
5.6%
M 857
 
5.5%
a 857
 
5.5%
T 572
 
3.6%
t 572
 
3.6%
Other values (16) 2858
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2707
17.3%
r 2520
16.1%
i 1636
10.4%
d 1167
7.4%
g 1043
 
6.7%
o 883
 
5.6%
M 857
 
5.5%
a 857
 
5.5%
T 572
 
3.6%
t 572
 
3.6%
Other values (16) 2858
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2707
17.3%
r 2520
16.1%
i 1636
10.4%
d 1167
7.4%
g 1043
 
6.7%
o 883
 
5.6%
M 857
 
5.5%
a 857
 
5.5%
T 572
 
3.6%
t 572
 
3.6%
Other values (16) 2858
18.2%

Income
Real number (ℝ)

HIGH CORRELATION 

Distinct1973
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51969.861
Minimum1730
Maximum162397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:10.533866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile18985
Q135284
median51373
Q368487
95-th percentile83977
Maximum162397
Range160667
Interquartile range (IQR)33203

Descriptive statistics

Standard deviation21526.32
Coefficient of variation (CV)0.41420776
Kurtosis0.71354882
Mean51969.861
Median Absolute Deviation (MAD)16549
Skewness0.34734968
Sum1.1511324 × 108
Variance4.6338246 × 108
MonotonicityDecreasing
2024-08-29T13:14:11.082924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500 12
 
0.5%
35860 4
 
0.2%
37760 3
 
0.1%
47025 3
 
0.1%
18929 3
 
0.1%
63841 3
 
0.1%
39922 3
 
0.1%
18690 3
 
0.1%
83844 3
 
0.1%
67445 3
 
0.1%
Other values (1963) 2175
98.2%
ValueCountFrequency (%)
1730 1
< 0.1%
2447 1
< 0.1%
3502 1
< 0.1%
4023 1
< 0.1%
4428 1
< 0.1%
4861 1
< 0.1%
5305 1
< 0.1%
5648 1
< 0.1%
6560 1
< 0.1%
6835 1
< 0.1%
ValueCountFrequency (%)
162397 1
< 0.1%
160803 1
< 0.1%
157733 1
< 0.1%
157243 1
< 0.1%
157146 1
< 0.1%
156924 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
105471 1
< 0.1%
102692 1
< 0.1%

Kidhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
1283 
1
886 
2
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2215
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1283
57.9%
1 886
40.0%
2 46
 
2.1%

Length

2024-08-29T13:14:11.437576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:11.718208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1283
57.9%
1 886
40.0%
2 46
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1283
57.9%
1 886
40.0%
2 46
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1283
57.9%
1 886
40.0%
2 46
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1283
57.9%
1 886
40.0%
2 46
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1283
57.9%
1 886
40.0%
2 46
 
2.1%

Teenhome
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
1146 
1
1018 
2
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2215
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1146
51.7%
1 1018
46.0%
2 51
 
2.3%

Length

2024-08-29T13:14:11.947289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:12.246866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1146
51.7%
1 1018
46.0%
2 51
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1146
51.7%
1 1018
46.0%
2 51
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1146
51.7%
1 1018
46.0%
2 51
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1146
51.7%
1 1018
46.0%
2 51
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1146
51.7%
1 1018
46.0%
2 51
 
2.3%
Distinct662
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Minimum2012-01-08 00:00:00
Maximum2014-12-06 00:00:00
2024-08-29T13:14:12.541672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:12.856962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Recency
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.024379
Minimum0
Maximum99
Zeros28
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:13.169329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124
median49
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.949608
Coefficient of variation (CV)0.59051452
Kurtosis-1.1995723
Mean49.024379
Median Absolute Deviation (MAD)25
Skewness0.00075811521
Sum108589
Variance838.0798
MonotonicityNot monotonic
2024-08-29T13:14:13.514226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 37
 
1.7%
30 32
 
1.4%
54 32
 
1.4%
46 31
 
1.4%
92 30
 
1.4%
65 30
 
1.4%
29 29
 
1.3%
3 29
 
1.3%
71 29
 
1.3%
49 29
 
1.3%
Other values (90) 1907
86.1%
ValueCountFrequency (%)
0 28
1.3%
1 24
1.1%
2 28
1.3%
3 29
1.3%
4 26
1.2%
5 15
0.7%
6 21
0.9%
7 12
0.5%
8 25
1.1%
9 24
1.1%
ValueCountFrequency (%)
99 17
0.8%
98 22
1.0%
97 20
0.9%
96 23
1.0%
95 18
0.8%
94 26
1.2%
93 21
0.9%
92 30
1.4%
91 18
0.8%
90 20
0.9%

MntWines
Real number (ℝ)

HIGH CORRELATION 

Distinct776
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.22528
Minimum0
Maximum1493
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:13.836405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q124
median175
Q3505
95-th percentile1000.3
Maximum1493
Range1493
Interquartile range (IQR)481

Descriptive statistics

Standard deviation337.34538
Coefficient of variation (CV)1.1052341
Kurtosis0.58149303
Mean305.22528
Median Absolute Deviation (MAD)166
Skewness1.1701833
Sum676074
Variance113801.91
MonotonicityNot monotonic
2024-08-29T13:14:14.161522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 42
 
1.9%
5 37
 
1.7%
1 37
 
1.7%
6 37
 
1.7%
4 33
 
1.5%
8 30
 
1.4%
3 30
 
1.4%
9 27
 
1.2%
12 25
 
1.1%
10 24
 
1.1%
Other values (766) 1893
85.5%
ValueCountFrequency (%)
0 13
 
0.6%
1 37
1.7%
2 42
1.9%
3 30
1.4%
4 33
1.5%
5 37
1.7%
6 37
1.7%
7 21
0.9%
8 30
1.4%
9 27
1.2%
ValueCountFrequency (%)
1493 1
< 0.1%
1492 2
0.1%
1486 1
< 0.1%
1478 2
0.1%
1462 1
< 0.1%
1459 1
< 0.1%
1449 1
< 0.1%
1396 1
< 0.1%
1394 1
< 0.1%
1379 1
< 0.1%

MntFruits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.361625
Minimum0
Maximum199
Zeros395
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:14.502802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q333
95-th percentile122.3
Maximum199
Range199
Interquartile range (IQR)31

Descriptive statistics

Standard deviation39.802036
Coefficient of variation (CV)1.5098476
Kurtosis4.050332
Mean26.361625
Median Absolute Deviation (MAD)8
Skewness2.1009139
Sum58391
Variance1584.202
MonotonicityNot monotonic
2024-08-29T13:14:14.831445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 395
 
17.8%
1 158
 
7.1%
2 119
 
5.4%
3 114
 
5.1%
4 103
 
4.7%
7 67
 
3.0%
6 62
 
2.8%
5 62
 
2.8%
12 50
 
2.3%
8 48
 
2.2%
Other values (148) 1037
46.8%
ValueCountFrequency (%)
0 395
17.8%
1 158
 
7.1%
2 119
 
5.4%
3 114
 
5.1%
4 103
 
4.7%
5 62
 
2.8%
6 62
 
2.8%
7 67
 
3.0%
8 48
 
2.2%
9 35
 
1.6%
ValueCountFrequency (%)
199 2
0.1%
197 1
 
< 0.1%
194 3
0.1%
193 2
0.1%
190 1
 
< 0.1%
189 1
 
< 0.1%
185 2
0.1%
184 1
 
< 0.1%
183 3
0.1%
181 1
 
< 0.1%

MntMeatProducts
Real number (ℝ)

HIGH CORRELATION 

Distinct554
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.06321
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:15.159638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median68
Q3232.5
95-th percentile687.6
Maximum1725
Range1725
Interquartile range (IQR)216.5

Descriptive statistics

Standard deviation224.31156
Coefficient of variation (CV)1.3426748
Kurtosis5.0525366
Mean167.06321
Median Absolute Deviation (MAD)60
Skewness2.024958
Sum370045
Variance50315.675
MonotonicityNot monotonic
2024-08-29T13:14:15.489686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 53
 
2.4%
5 50
 
2.3%
11 49
 
2.2%
8 45
 
2.0%
6 42
 
1.9%
10 40
 
1.8%
3 39
 
1.8%
9 37
 
1.7%
16 35
 
1.6%
12 34
 
1.5%
Other values (544) 1791
80.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14
 
0.6%
2 30
1.4%
3 39
1.8%
4 30
1.4%
5 50
2.3%
6 42
1.9%
7 53
2.4%
8 45
2.0%
9 37
1.7%
ValueCountFrequency (%)
1725 2
0.1%
1622 1
< 0.1%
1582 1
< 0.1%
984 1
< 0.1%
981 1
< 0.1%
974 1
< 0.1%
968 1
< 0.1%
961 1
< 0.1%
951 2
0.1%
946 1
< 0.1%

MntFishProducts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct182
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.651016
Minimum0
Maximum259
Zeros379
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:15.817612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile169
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.760822
Coefficient of variation (CV)1.4544315
Kurtosis3.0734309
Mean37.651016
Median Absolute Deviation (MAD)12
Skewness1.9156555
Sum83397
Variance2998.7476
MonotonicityNot monotonic
2024-08-29T13:14:16.144685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 379
 
17.1%
2 152
 
6.9%
3 128
 
5.8%
4 108
 
4.9%
6 81
 
3.7%
7 66
 
3.0%
8 57
 
2.6%
10 54
 
2.4%
13 48
 
2.2%
11 46
 
2.1%
Other values (172) 1096
49.5%
ValueCountFrequency (%)
0 379
17.1%
1 10
 
0.5%
2 152
6.9%
3 128
 
5.8%
4 108
 
4.9%
5 1
 
< 0.1%
6 81
 
3.7%
7 66
 
3.0%
8 57
 
2.6%
10 54
 
2.4%
ValueCountFrequency (%)
259 1
 
< 0.1%
258 3
0.1%
254 1
 
< 0.1%
253 1
 
< 0.1%
250 3
0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
242 1
 
< 0.1%
240 2
0.1%
237 2
0.1%

MntSweetProducts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct176
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.040632
Minimum0
Maximum262
Zeros413
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:16.462538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile125.3
Maximum262
Range262
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.077594
Coefficient of variation (CV)1.5191063
Kurtosis4.1030327
Mean27.040632
Median Absolute Deviation (MAD)8
Skewness2.1026819
Sum59895
Variance1687.3687
MonotonicityNot monotonic
2024-08-29T13:14:16.816064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
 
18.6%
1 160
 
7.2%
2 123
 
5.6%
3 101
 
4.6%
4 80
 
3.6%
5 65
 
2.9%
6 63
 
2.8%
7 57
 
2.6%
8 56
 
2.5%
12 45
 
2.0%
Other values (166) 1052
47.5%
ValueCountFrequency (%)
0 413
18.6%
1 160
 
7.2%
2 123
 
5.6%
3 101
 
4.6%
4 80
 
3.6%
5 65
 
2.9%
6 63
 
2.8%
7 57
 
2.6%
8 56
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
262 1
 
< 0.1%
198 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 3
0.1%
192 3
0.1%
191 1
 
< 0.1%
189 2
0.1%
188 1
 
< 0.1%

MntGoldProds
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct212
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.979684
Minimum0
Maximum321
Zeros61
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:17.133917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median25
Q356
95-th percentile165.3
Maximum321
Range321
Interquartile range (IQR)47

Descriptive statistics

Standard deviation51.82266
Coefficient of variation (CV)1.1783318
Kurtosis3.1535661
Mean43.979684
Median Absolute Deviation (MAD)19
Skewness1.8385606
Sum97415
Variance2685.5881
MonotonicityNot monotonic
2024-08-29T13:14:17.447180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 71
 
3.2%
4 69
 
3.1%
3 68
 
3.1%
5 63
 
2.8%
2 62
 
2.8%
12 62
 
2.8%
0 61
 
2.8%
6 55
 
2.5%
7 52
 
2.3%
10 49
 
2.2%
Other values (202) 1603
72.4%
ValueCountFrequency (%)
0 61
2.8%
1 71
3.2%
2 62
2.8%
3 68
3.1%
4 69
3.1%
5 63
2.8%
6 55
2.5%
7 52
2.3%
8 40
1.8%
9 43
1.9%
ValueCountFrequency (%)
321 1
 
< 0.1%
291 1
 
< 0.1%
262 1
 
< 0.1%
249 1
 
< 0.1%
248 1
 
< 0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
245 1
 
< 0.1%
242 2
 
0.1%
241 6
0.3%

NumDealsPurchases
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3227991
Minimum0
Maximum15
Zeros44
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:17.768615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.92382
Coefficient of variation (CV)0.82823349
Kurtosis8.9808613
Mean2.3227991
Median Absolute Deviation (MAD)1
Skewness2.4168526
Sum5145
Variance3.7010834
MonotonicityNot monotonic
2024-08-29T13:14:18.008151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 960
43.3%
2 493
22.3%
3 293
 
13.2%
4 187
 
8.4%
5 94
 
4.2%
6 60
 
2.7%
0 44
 
2.0%
7 39
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
Other values (5) 23
 
1.0%
ValueCountFrequency (%)
0 44
 
2.0%
1 960
43.3%
2 493
22.3%
3 293
 
13.2%
4 187
 
8.4%
5 94
 
4.2%
6 60
 
2.7%
7 39
 
1.8%
8 14
 
0.6%
9 8
 
0.4%
ValueCountFrequency (%)
15 7
 
0.3%
13 3
 
0.1%
12 3
 
0.1%
11 5
 
0.2%
10 5
 
0.2%
9 8
 
0.4%
8 14
 
0.6%
7 39
1.8%
6 60
2.7%
5 94
4.2%

NumWebPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0857788
Minimum0
Maximum27
Zeros48
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:18.288770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7414729
Coefficient of variation (CV)0.67097929
Kurtosis4.0690826
Mean4.0857788
Median Absolute Deviation (MAD)2
Skewness1.1963858
Sum9050
Variance7.5156739
MonotonicityNot monotonic
2024-08-29T13:14:18.570426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 368
16.6%
1 348
15.7%
3 333
15.0%
4 277
12.5%
5 219
9.9%
6 201
9.1%
7 154
7.0%
8 102
 
4.6%
9 75
 
3.4%
0 48
 
2.2%
Other values (5) 90
 
4.1%
ValueCountFrequency (%)
0 48
 
2.2%
1 348
15.7%
2 368
16.6%
3 333
15.0%
4 277
12.5%
5 219
9.9%
6 201
9.1%
7 154
7.0%
8 102
 
4.6%
9 75
 
3.4%
ValueCountFrequency (%)
27 1
 
< 0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
11 44
 
2.0%
10 43
 
1.9%
9 75
 
3.4%
8 102
4.6%
7 154
7.0%
6 201
9.1%
5 219
9.9%

NumCatalogPurchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6717833
Minimum0
Maximum28
Zeros576
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:18.852581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.927179
Coefficient of variation (CV)1.09559
Kurtosis8.0634086
Mean2.6717833
Median Absolute Deviation (MAD)2
Skewness1.8803768
Sum5918
Variance8.5683769
MonotonicityNot monotonic
2024-08-29T13:14:19.081999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 576
26.0%
1 491
22.2%
2 274
12.4%
3 182
 
8.2%
4 181
 
8.2%
5 137
 
6.2%
6 128
 
5.8%
7 79
 
3.6%
8 55
 
2.5%
10 47
 
2.1%
Other values (4) 65
 
2.9%
ValueCountFrequency (%)
0 576
26.0%
1 491
22.2%
2 274
12.4%
3 182
 
8.2%
4 181
 
8.2%
5 137
 
6.2%
6 128
 
5.8%
7 79
 
3.6%
8 55
 
2.5%
9 42
 
1.9%
ValueCountFrequency (%)
28 3
 
0.1%
22 1
 
< 0.1%
11 19
 
0.9%
10 47
 
2.1%
9 42
 
1.9%
8 55
 
2.5%
7 79
3.6%
6 128
5.8%
5 137
6.2%
4 181
8.2%

NumStorePurchases
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8022573
Minimum0
Maximum13
Zeros14
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:19.331604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2509736
Coefficient of variation (CV)0.56029463
Kurtosis-0.62728326
Mean5.8022573
Median Absolute Deviation (MAD)2
Skewness0.70114237
Sum12852
Variance10.56883
MonotonicityNot monotonic
2024-08-29T13:14:19.617842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 483
21.8%
4 319
14.4%
2 220
9.9%
5 211
9.5%
6 177
 
8.0%
8 147
 
6.6%
7 141
 
6.4%
10 124
 
5.6%
9 106
 
4.8%
12 104
 
4.7%
Other values (4) 183
 
8.3%
ValueCountFrequency (%)
0 14
 
0.6%
1 6
 
0.3%
2 220
9.9%
3 483
21.8%
4 319
14.4%
5 211
9.5%
6 177
 
8.0%
7 141
 
6.4%
8 147
 
6.6%
9 106
 
4.8%
ValueCountFrequency (%)
13 83
 
3.7%
12 104
 
4.7%
11 80
 
3.6%
10 124
 
5.6%
9 106
 
4.8%
8 147
6.6%
7 141
6.4%
6 177
8.0%
5 211
9.5%
4 319
14.4%

NumWebVisitsMonth
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3187359
Minimum0
Maximum20
Zeros10
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-08-29T13:14:19.898377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.425863
Coefficient of variation (CV)0.45609767
Kurtosis1.8508419
Mean5.3187359
Median Absolute Deviation (MAD)2
Skewness0.21837609
Sum11781
Variance5.8848114
MonotonicityNot monotonic
2024-08-29T13:14:20.173420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 387
17.5%
8 340
15.3%
6 334
15.1%
5 279
12.6%
4 217
9.8%
3 203
9.2%
2 201
9.1%
1 150
 
6.8%
9 82
 
3.7%
0 10
 
0.5%
Other values (6) 12
 
0.5%
ValueCountFrequency (%)
0 10
 
0.5%
1 150
 
6.8%
2 201
9.1%
3 203
9.2%
4 217
9.8%
5 279
12.6%
6 334
15.1%
7 387
17.5%
8 340
15.3%
9 82
 
3.7%
ValueCountFrequency (%)
20 3
 
0.1%
19 2
 
0.1%
17 1
 
< 0.1%
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 82
 
3.7%
8 340
15.3%
7 387
17.5%
6 334
15.1%

AcceptedCmp3
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
2052 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2215
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2052
92.6%
1 163
 
7.4%

Length

2024-08-29T13:14:20.478555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:20.794477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2052
92.6%
1 163
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 2052
92.6%
1 163
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2052
92.6%
1 163
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2052
92.6%
1 163
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2052
92.6%
1 163
 
7.4%

AcceptedCmp4
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
2051 
1
 
164

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2215
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2051
92.6%
1 164
 
7.4%

Length

2024-08-29T13:14:21.029660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:21.404909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2051
92.6%
1 164
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 2051
92.6%
1 164
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2051
92.6%
1 164
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2051
92.6%
1 164
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2051
92.6%
1 164
 
7.4%

AcceptedCmp5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
2053 
1
 
162

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2215
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2053
92.7%
1 162
 
7.3%

Length

2024-08-29T13:14:21.825165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:22.240212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2053
92.7%
1 162
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2053
92.7%
1 162
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2053
92.7%
1 162
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2053
92.7%
1 162
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2053
92.7%
1 162
 
7.3%

AcceptedCmp1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
2073 
1
 
142

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2215
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2073
93.6%
1 142
 
6.4%

Length

2024-08-29T13:14:22.617542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:23.089925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2073
93.6%
1 142
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 2073
93.6%
1 142
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2073
93.6%
1 142
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2073
93.6%
1 142
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2073
93.6%
1 142
 
6.4%

AcceptedCmp2
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
2185 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2215
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2185
98.6%
1 30
 
1.4%

Length

2024-08-29T13:14:23.446321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:23.935077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2185
98.6%
1 30
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 2185
98.6%
1 30
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2185
98.6%
1 30
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2185
98.6%
1 30
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2185
98.6%
1 30
 
1.4%

Complain
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
2194 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2215
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2194
99.1%
1 21
 
0.9%

Length

2024-08-29T13:14:24.392559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:24.910094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2194
99.1%
1 21
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 2194
99.1%
1 21
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2194
99.1%
1 21
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2194
99.1%
1 21
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2194
99.1%
1 21
 
0.9%

Z_CostContact
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
3
2215 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2215
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 2215
100.0%

Length

2024-08-29T13:14:25.200553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:25.465050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 2215
100.0%

Most occurring characters

ValueCountFrequency (%)
3 2215
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2215
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2215
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2215
100.0%

Z_Revenue
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
11
2215 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 2215
100.0%

Length

2024-08-29T13:14:25.681088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:25.948946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
11 2215
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4430
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4430
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4430
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4430
100.0%

Response
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
1882 
1
333 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2215
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1882
85.0%
1 333
 
15.0%

Length

2024-08-29T13:14:26.178123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-29T13:14:26.458024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1882
85.0%
1 333
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 1882
85.0%
1 333
 
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1882
85.0%
1 333
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1882
85.0%
1 333
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1882
85.0%
1 333
 
15.0%

Interactions

2024-08-29T13:13:59.908705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:51.509945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:56.680516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:02.287803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:06.647764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:10.886668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:17.145289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:21.496497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:25.973953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:31.198033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:35.320621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:42.045362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:46.536612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:50.534168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:54.982991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:00.191423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:51.788090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:56.943792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:02.561525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:06.906889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:11.151233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:17.412704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:21.771323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:26.377836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:31.466041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:35.581248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:42.424220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:46.795022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:50.795865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:55.368601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:00.474672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:52.051245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:57.236401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:02.834579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:07.185192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:11.526850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:17.689141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:22.039021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:26.725949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:31.747531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:35.853547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:42.845152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:47.045423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:51.044642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:55.783075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:00.775987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:52.368680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:57.702630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:03.124411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:07.484662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:11.938956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:17.997123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:22.357104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:27.139603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:32.028222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:36.134736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:43.296348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:47.332377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:51.321107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:56.186467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:01.056192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:52.636339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:58.131093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:03.420953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:07.775629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:12.364254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:18.271577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:22.629883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:27.533752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:32.304159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:36.406853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:43.553713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:47.583237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:51.587886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:56.603925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:01.341369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:52.922750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:58.564790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:03.723763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:08.082036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:12.800721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:18.565176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:22.905861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:27.913799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:32.579504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:36.669422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:43.821232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:47.845081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:51.856651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:57.018269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:01.633004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:53.210574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:59.022233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:04.016455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:08.407150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:13.217657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:18.858440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:23.219046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:28.310104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:32.869890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:36.960968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:44.101299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:48.112899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:52.127455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:57.397013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:01.918160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:53.499949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:59.417377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:04.335221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:08.702789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:13.660155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:19.166783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:23.501860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:28.735096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:33.138170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:37.367336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:44.408263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:48.390184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:52.393137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:57.672542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:02.206225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:53.764356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:59.801259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:04.606026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:08.971210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:14.098100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:19.448715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:23.773286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:29.184021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:33.410755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:38.160966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:44.665640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:48.649150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:52.652194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:57.956271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:02.463669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:54.042791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:00.190920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:04.878850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:09.251863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:14.490185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:19.727328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:24.039825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:29.549373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:33.688270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:39.748476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:44.920316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:48.899206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:52.901894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:58.217275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:02.729999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:54.336167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:00.603449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:05.161545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:09.516177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:14.938081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:20.007617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:24.331061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:29.814309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:33.948329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:40.171897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:45.197134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:49.153865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:53.158564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:58.502246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:03.020128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:54.631794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:01.061485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:05.456883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:09.800404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:15.396102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:20.326995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:24.620106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:30.092082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:34.227134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:40.584645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:45.461168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:49.441516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:53.506006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:58.786088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:03.305120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:54.911127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:01.423247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:05.742746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:10.058622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:15.671162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:20.614704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:24.898697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:30.358767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:34.492735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:40.864144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:45.721161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:49.697976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:53.887636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:59.060638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:03.570782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:55.199640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:01.695930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:06.022748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:10.330597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:16.573698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:20.895722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:25.184062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:30.634656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:34.761655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:41.263770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:45.980770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:49.950227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:54.202053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:59.337347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:14:03.842151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:12:55.502555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:01.968416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:06.332808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:10.605212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:16.843377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:21.202651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:25.563347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:30.908204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:35.028668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:41.670195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:46.259558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:50.234182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:54.599917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-29T13:13:59.612293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-08-29T13:14:26.718891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AcceptedCmp1AcceptedCmp2AcceptedCmp3AcceptedCmp4AcceptedCmp5ComplainEducationIDIncomeKidhomeMarital_StatusMntFishProductsMntFruitsMntGoldProdsMntMeatProductsMntSweetProductsMntWinesNumCatalogPurchasesNumDealsPurchasesNumStorePurchasesNumWebPurchasesNumWebVisitsMonthRecencyResponseTeenhomeYear_Birth
AcceptedCmp11.0000.1670.0900.2380.4040.0000.0320.0440.4000.1840.0290.2690.2590.1980.3100.2560.3560.3150.1660.1950.1670.2040.0000.2940.1470.053
AcceptedCmp20.1671.0000.0610.2870.2140.0000.0170.0360.1440.0790.0000.0480.0000.0850.0340.0470.3010.1110.0000.0810.0000.0000.0340.1620.0000.000
AcceptedCmp30.0900.0611.0000.0730.0740.0000.0000.0000.0640.0300.0000.0770.0000.1250.0270.0000.0940.0880.0000.1790.0220.0780.0430.2510.0380.053
AcceptedCmp40.2380.2870.0731.0000.3070.0000.0530.0000.2660.1620.0000.0090.0690.0680.0970.0250.3970.1900.0590.2140.1590.0000.0000.1770.0240.052
AcceptedCmp50.4040.2140.0740.3071.0000.0000.0340.0000.5640.2090.0200.2660.2840.1810.3770.2670.5180.3600.2450.2280.1710.3080.0000.3200.2050.083
Complain0.0000.0000.0000.0000.0001.0000.0390.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.138
Education0.0320.0170.0000.0530.0340.0391.0000.0000.1820.0510.0000.0640.0710.0650.0550.0690.1150.0650.0000.1070.0830.0540.0000.0930.1050.113
ID0.0440.0360.0000.0000.0000.0000.0001.0000.0030.0040.000-0.030-0.020-0.041-0.013-0.032-0.024-0.011-0.028-0.021-0.024-0.012-0.0430.0340.0000.005
Income0.4000.1440.0640.2660.5640.0000.1820.0031.0000.4040.0000.5780.5820.5060.8180.5680.8320.793-0.1970.7330.574-0.6440.0090.2570.330-0.218
Kidhome0.1840.0790.0300.1620.2090.0280.0510.0040.4041.0000.0370.3240.3140.2700.3220.2940.4070.3870.2120.4030.2940.3450.0660.0730.0510.221
Marital_Status0.0290.0000.0000.0000.0200.0000.0000.0000.0000.0371.0000.0520.0290.0590.0310.0000.0150.0000.0180.0240.0360.0000.0260.1450.0760.091
MntFishProducts0.2690.0480.0770.0090.2660.0000.064-0.0300.5780.3240.0521.0000.7040.5650.7260.7000.5220.656-0.1240.5810.466-0.4600.0130.1280.140-0.030
MntFruits0.2590.0000.0000.0690.2840.0000.071-0.0200.5820.3140.0290.7041.0000.5700.7140.6910.5170.633-0.1120.5820.473-0.4440.0250.1520.121-0.026
MntGoldProds0.1980.0850.1250.0680.1810.0000.065-0.0410.5060.2700.0590.5650.5701.0000.6390.5410.5750.6490.0900.5400.577-0.2580.0170.1580.062-0.076
MntMeatProducts0.3100.0340.0270.0970.3770.0000.055-0.0130.8180.3220.0310.7260.7140.6391.0000.6980.8240.854-0.0340.7800.683-0.4940.0260.2410.226-0.113
MntSweetProducts0.2560.0470.0000.0250.2670.0000.069-0.0320.5680.2940.0000.7000.6910.5410.6981.0000.5050.628-0.1080.5810.462-0.4490.0240.1110.1010.002
MntWines0.3560.3010.0940.3970.5180.0000.115-0.0240.8320.4070.0150.5220.5170.5750.8240.5051.0000.8230.0540.8050.743-0.3910.0160.2670.112-0.234
NumCatalogPurchases0.3150.1110.0880.1900.3600.0000.065-0.0110.7930.3870.0000.6560.6330.6490.8540.6280.8231.000-0.0440.7070.621-0.5390.0280.2190.119-0.178
NumDealsPurchases0.1660.0000.0000.0590.2450.0000.000-0.028-0.1970.2120.018-0.124-0.1120.090-0.034-0.1080.054-0.0441.0000.0970.2850.3960.0090.0980.347-0.085
NumStorePurchases0.1950.0810.1790.2140.2280.0000.107-0.0210.7330.4030.0240.5810.5820.5400.7800.5810.8050.7070.0971.0000.674-0.4580.0030.1490.084-0.166
NumWebPurchases0.1670.0000.0220.1590.1710.0000.083-0.0240.5740.2940.0360.4660.4730.5770.6830.4620.7430.6210.2850.6741.000-0.097-0.0020.1640.160-0.166
NumWebVisitsMonth0.2040.0000.0780.0000.3080.0000.054-0.012-0.6440.3450.000-0.460-0.444-0.258-0.494-0.449-0.391-0.5390.396-0.458-0.0971.000-0.0190.1210.2170.134
Recency0.0000.0340.0430.0000.0000.0000.000-0.0430.0090.0660.0260.0130.0250.0170.0260.0240.0160.0280.0090.003-0.002-0.0191.0000.2090.050-0.017
Response0.2940.1620.2510.1770.3200.0000.0930.0340.2570.0730.1450.1280.1520.1580.2410.1110.2670.2190.0980.1490.1640.1210.2091.0000.1580.000
Teenhome0.1470.0000.0380.0240.2050.0000.1050.0000.3300.0510.0760.1400.1210.0620.2260.1010.1120.1190.3470.0840.1600.2170.0500.1581.0000.306
Year_Birth0.0530.0000.0530.0520.0830.1380.1130.005-0.2180.2210.091-0.030-0.026-0.076-0.1130.002-0.234-0.178-0.085-0.166-0.1660.134-0.0170.0000.3061.000

Missing values

2024-08-29T13:14:04.357071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-29T13:14:05.236910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainZ_CostContactZ_RevenueResponse
015031976PhDTogether1623971103-06-20133185116212000110000003110
115011982PhDMarried1608030004-08-20122155161622173415028100000003110
253361971MasterTogether1577331004-06-2013373919208010110000003110
384751973PhDMarried1572430101-03-201498202158212115022000000003110
449311977GraduationTogether1571460029-04-2013131017252110028010000003110
5111811949PhDMarried1569240029-08-201385212111000000000003110
655551975GraduationDivorced1539240007-02-201481111111000000000003110
746191945PhDSingle1137340028-05-20149623126230270010000003110
846111970GraduationTogether1054710021-01-2013361009181104202212070981330011003111
9100891974GraduationDivorced1026920005-04-20135168148444321721481691320111103111
IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainZ_CostContactZ_RevenueResponse
220543031957PhDTogether68350108-12-20127610721222120001200000003110
220642461982MasterSingle65600012-12-20132671126432620101170000003110
220758991950PhDTogether56481113-03-201468280611131100201000003110
220893031976GraduationMarried53050130-07-20131212471350100130000003110
220939551965GraduationDivorced48610022-06-2014202111010000140000003110
2210103111969GraduationMarried44280105-10-2013016412243210250010000003110
221199311963PhDMarried40231123-06-20142950111115000190000003110
2212111101973GraduationSingle35021013-04-2013562110010000140000003110
221353761979GraduationMarried24471006-01-20134211172511115028010000003110
221468621971GraduationDivorced17300018-05-20146511311115000200000003110